A method includes obtaining multiple geolocation measurements, where each geolocation measurement is generated using cross-ambiguity function (CAF) detection. The geolocation measurements are associated with at least one signal from at least one signal source and received by multiple receivers. The method also includes associating related geolocation measurements to form at least one collection of related geolocation measurements, where each collection of related geolocation measurements is associated with a common one of the at least one signal received by at least some of the receivers. The method further includes performing geolocation using the at least one collection of related geolocation measurements to identify one or more geolocations of the at least one signal source.
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1. A method comprising: obtaining multiple geolocation measurements, each geolocation measurement generated using cross-ambiguity function (CAF) detection, the geolocation measurements associated with at least one signal from at least one signal source and received by multiple receivers; associating related geolocation measurements to form at least one collection of related geolocation measurements, each collection of related geolocation measurements associated with a common one of the at least one signal received by at least some of the receivers; and performing geolocation using the at least one collection of related geolocation measurements to identify one or more geolocations of the at least one signal source; wherein associating the related geolocation measurements comprises: placing the geolocation measurements into different buckets, the geolocation measurements in each bucket associated with a common area of interest and based on signal receipt within a specified time window; placing the geolocation measurements from the buckets into different groups, each group containing geolocation measurements from the same bucket, the geolocation measurements in each group associated with an approximately equal pulse width and an approximately equal frequency; and placing the geolocation measurements from the groups into different subgroups, each subgroup containing geolocation measurements from the same group, the geolocation measurements in each subgroup associated with a common pair of receivers and associated with approximately equal time difference of arrival (TDOA) or frequency difference of arrival (FDOA) measurements.
This invention relates to geolocation techniques for identifying the position of signal sources using multiple receivers. The problem addressed is the accurate and efficient determination of signal source locations from multiple geolocation measurements, particularly when dealing with signals received by different receivers at different times. The method involves obtaining geolocation measurements generated using cross-ambiguity function (CAF) detection, where each measurement is associated with a signal from one or more signal sources and received by multiple receivers. The measurements are then processed to associate related measurements into collections, which are used to identify the geolocations of the signal sources. The association process involves organizing the measurements into buckets, groups, and subgroups based on common criteria. Measurements are first placed into buckets corresponding to a common area of interest and a specified time window. Within each bucket, measurements are further grouped based on similar pulse width and frequency. Finally, measurements within each group are divided into subgroups based on common receiver pairs and similar time difference of arrival (TDOA) or frequency difference of arrival (FDOA) measurements. This hierarchical organization improves the accuracy and reliability of geolocation by ensuring that related measurements are processed together, reducing errors from unrelated or inconsistent data. The method is particularly useful in applications requiring precise signal source localization, such as radar, communications, and surveillance systems.
2. The method of claim 1 , further comprising at least one of: generating a graphical display based on at least one of the one or more geolocations; and generating a report containing at least one of the one or more geolocations.
This invention relates to a system for tracking and analyzing geolocation data, addressing the need for efficient collection, processing, and visualization of location-based information. The system captures geolocation data from one or more sources, such as mobile devices, sensors, or network logs, and processes this data to extract relevant spatial and temporal information. The processed data is then used to generate visual representations, such as maps or charts, to display the geolocations in a user-friendly format. Additionally, the system can produce detailed reports summarizing the geolocation data, including statistical analyses, trends, or patterns. The visual displays and reports help users monitor, analyze, and interpret the geolocation data for applications like asset tracking, security monitoring, or environmental studies. The system may also include features for filtering, sorting, or categorizing the geolocation data to enhance usability and accuracy. By integrating these functionalities, the invention provides a comprehensive solution for managing and leveraging geospatial information in various industries.
3. The method of claim 1 , wherein associating the related geolocation measurements further comprises forming multiple observation groups, each observation group containing geolocation measurements that might be related to one another.
This invention relates to geolocation measurement processing, specifically improving the accuracy and reliability of geolocation data by grouping related measurements. The problem addressed is the challenge of distinguishing between relevant and irrelevant geolocation measurements, particularly in environments where multiple signals or sources may interfere with accurate positioning. The solution involves forming multiple observation groups, where each group contains geolocation measurements that are likely related to one another. By organizing measurements into these groups, the system can better identify patterns, filter noise, and improve the overall precision of geolocation determinations. The method may also include additional steps such as filtering, weighting, or combining measurements within each group to enhance accuracy. This approach is particularly useful in applications requiring high-precision geolocation, such as navigation systems, asset tracking, or location-based services, where distinguishing between relevant and irrelevant data is critical for reliable performance. The grouping process helps reduce errors caused by spurious signals or environmental factors, leading to more consistent and trustworthy geolocation results.
4. The method of claim 3 , wherein associating the related geolocation measurements further comprises: identifying geolocation measurements from the observation groups satisfying one or more criteria; and placing the identified geolocation measurements into one or more refined observation groups, each refined observation group containing a collection of related geolocation measurements.
This invention relates to geolocation measurement processing, specifically refining groups of geolocation measurements to improve accuracy and relevance. The problem addressed is the challenge of organizing and filtering geolocation data to extract meaningful patterns or relationships from raw observations. The method involves analyzing groups of geolocation measurements to identify subsets that meet specific criteria, such as proximity, time correlation, or other relevance factors. These identified measurements are then reassigned into refined observation groups, where each group contains only related geolocation measurements. This refinement process enhances the precision of geolocation data by isolating relevant subsets from broader, less specific observation groups. The criteria used for identification may include spatial proximity, temporal alignment, or other contextual factors that indicate a meaningful relationship between measurements. By refining the observation groups, the method ensures that each group contains only measurements that are logically or contextually connected, improving the reliability of subsequent analysis or applications relying on this data. This approach is particularly useful in applications requiring high-accuracy geolocation data, such as navigation systems, asset tracking, or location-based services, where distinguishing relevant measurements from noise or unrelated data is critical. The refinement process helps eliminate irrelevant or outlier measurements, resulting in more accurate and actionable geolocation information.
5. The method of claim 4 , wherein the one or more criteria include at least one of: a criterion that each collection of related geolocation measurements includes one geolocation measurement from each of multiple distinct pairs of receivers; and a criterion that each collection of related geolocation measurements has TDOA or FDOA measurements that sum to a value within a threshold amount of zero.
This invention relates to geolocation measurement techniques, specifically improving the accuracy and reliability of geolocation estimates by analyzing collections of related measurements. The problem addressed is the inherent noise and errors in geolocation data, particularly when using time difference of arrival (TDOA) or frequency difference of arrival (FDOA) measurements from multiple receivers. These errors can lead to inaccurate position estimates, especially in scenarios where measurements are corrupted by multipath effects or other interference. The invention describes a method for processing geolocation measurements to filter out unreliable data. The method involves evaluating collections of related geolocation measurements based on predefined criteria. One criterion ensures that each collection includes at least one measurement from each of multiple distinct pairs of receivers, ensuring diversity in the data sources. Another criterion checks that the TDOA or FDOA measurements within a collection sum to a value within a predefined threshold of zero, which helps identify and discard inconsistent or erroneous measurements. By applying these criteria, the method improves the reliability of the geolocation estimates by excluding measurements that would otherwise degrade accuracy. The approach is particularly useful in applications requiring high-precision geolocation, such as military tracking, emergency response, or asset monitoring.
6. The method of claim 4 , wherein associating the related geolocation measurements further comprises: identifying geolocation measurements from the observation groups that are associated with prior geolocation measurements previously used to determine at least one prior geolocation; and placing the identified geolocation measurements into one or more additional refined observation groups.
This invention relates to geolocation measurement refinement in positioning systems. The problem addressed is improving the accuracy of geolocation determinations by refining raw geolocation measurements through iterative grouping and analysis. The method involves processing geolocation measurements from multiple observation groups, where each group contains measurements from different sources or time periods. The refinement process identifies measurements within these groups that are associated with prior geolocation determinations, meaning they share common characteristics or spatial relationships with previously validated measurements. These identified measurements are then separated into additional refined observation groups, allowing for more precise geolocation calculations. The refinement step ensures that only relevant and reliable measurements are used in subsequent positioning calculations, reducing errors caused by outliers or inconsistent data. This iterative approach enhances the accuracy of geolocation systems by progressively filtering and refining measurement data. The method is particularly useful in applications requiring high-precision positioning, such as navigation, asset tracking, or surveying, where measurement noise and environmental factors can degrade accuracy. By leveraging prior geolocation data, the system dynamically improves its ability to filter and refine incoming measurements, leading to more reliable positioning results.
7. The method of claim 6 , wherein associating the related geolocation measurements further comprises: identifying remaining geolocation measurements from the observation groups that do not satisfy the one or more criteria and that are not associated with the prior geolocation measurements; and determining whether any of the remaining geolocation measurements are related to one another.
This invention relates to geolocation measurement processing, specifically improving the accuracy and reliability of geolocation data by associating related measurements. The problem addressed is the presence of redundant or conflicting geolocation measurements in datasets, which can lead to inaccuracies in applications such as navigation, tracking, or mapping. The solution involves grouping geolocation measurements into observation groups and associating them based on predefined criteria to filter out unreliable data. The method first processes geolocation measurements by grouping them into observation groups, where each group contains measurements that share common characteristics, such as time, location, or source. These groups are then analyzed to identify prior geolocation measurements that meet specific criteria, such as signal strength, accuracy thresholds, or consistency with other measurements. Measurements that do not meet these criteria are flagged as remaining geolocation measurements. The method further refines the association process by determining whether any of these remaining measurements are related to one another. This step ensures that even measurements initially deemed unreliable may still be relevant if they correlate with other measurements. By systematically associating related measurements, the method enhances the overall accuracy of the geolocation dataset, reducing errors and improving the reliability of geolocation-based applications.
8. An apparatus comprising: at least one memory configured to store multiple geolocation measurements based on cross-ambiguity function (CAF) detection, the geolocation measurements associated with at least one signal from at least one signal source that is received by multiple receivers; and at least one processing device configured to: associate related geolocation measurements to form at least one collection of related geolocation measurements, each collection of related geolocation measurements associated with a common one of the at least one signal received by at least some of the receivers; and perform geolocation using the at least one collection of related geolocation measurements to identify one or more geolocations of the at least one signal source; wherein, to associate the related geolocation measurements, the at least one processing device is configured to: place the geolocation measurements into different buckets, the geolocation measurements in each bucket associated with a common area of interest and based on signal receipt within a specified time window; place the geolocation measurements from the buckets into different groups, each group containing geolocation measurements from the same bucket, the geolocation measurements in each group associated with an approximately equal pulse width and an approximately equal frequency; and place the geolocation measurements from the groups into different subgroups, each subgroup containing geolocation measurements from the same group, the geolocation measurements in each subgroup associated with a common pair of receivers and associated with approximately equal time difference of arrival (TDOA) or frequency difference of arrival (FDOA) measurements.
This invention relates to geolocation systems that use cross-ambiguity function (CAF) detection to determine the position of signal sources. The system addresses challenges in accurately identifying and tracking signal sources by processing multiple geolocation measurements from multiple receivers. The apparatus includes memory to store geolocation measurements derived from CAF detection, where these measurements are associated with signals received from one or more signal sources by multiple receivers. A processing device processes these measurements by organizing them into collections based on shared characteristics. First, measurements are placed into buckets corresponding to a common area of interest and a specified time window. Next, measurements within each bucket are grouped by similar pulse width and frequency. Finally, measurements within each group are further divided into subgroups based on the same pair of receivers and similar time difference of arrival (TDOA) or frequency difference of arrival (FDOA) measurements. The processed collections of related measurements are then used to perform geolocation, identifying one or more positions of the signal source. This method improves accuracy by systematically filtering and associating measurements to reduce noise and improve signal source localization.
9. The apparatus of claim 8 , wherein, to associate the related geolocation measurements, the at least one processing device is further configured to form multiple observation groups, each observation group containing geolocation measurements that might be related to one another.
This invention relates to geolocation measurement systems, specifically addressing the challenge of accurately associating related geolocation measurements from multiple sources or devices. The system includes at least one processing device configured to collect geolocation measurements from various sources, such as sensors or devices, and analyze these measurements to determine their potential relationships. The processing device forms multiple observation groups, where each group contains geolocation measurements that may be related to one another based on spatial, temporal, or contextual similarities. By organizing measurements into these groups, the system improves the accuracy and reliability of geolocation data analysis, enabling better tracking, navigation, or mapping applications. The invention enhances the ability to distinguish between relevant and irrelevant measurements, reducing errors in geolocation-based services. The processing device may also apply additional filtering or validation techniques to refine the grouping process, ensuring that only meaningful associations are considered. This approach is particularly useful in environments where multiple devices or sensors generate geolocation data, such as in IoT networks, autonomous vehicle systems, or asset tracking applications. The system's ability to dynamically group and analyze measurements improves the overall efficiency and accuracy of geolocation-based operations.
10. The apparatus of claim 9 , wherein, to associate the related geolocation measurements, the at least one processing device is further configured to: identify geolocation measurements from the observation groups satisfying one or more criteria; and place the identified geolocation measurements into one or more refined observation groups, each refined observation group containing a collection of related geolocation measurements.
This invention relates to a system for processing geolocation measurements to improve accuracy and reliability. The system addresses the challenge of accurately determining the position of a device or object by analyzing multiple geolocation measurements and grouping related measurements to enhance precision. The apparatus includes at least one processing device configured to receive geolocation measurements from various sources, such as GPS, Wi-Fi, or cellular signals. These measurements are initially organized into observation groups based on their source or other initial criteria. To refine these groups, the processing device identifies measurements that satisfy specific criteria, such as proximity in time, spatial proximity, or signal strength consistency. The identified measurements are then placed into refined observation groups, where each group contains a collection of related measurements that are more likely to represent the true position of the device or object. By refining the observation groups, the system improves the accuracy of geolocation determinations by filtering out outliers and consolidating measurements that are more likely to be correct. This process enhances the reliability of position tracking in applications such as navigation, asset tracking, or location-based services. The refined groups can then be used for further analysis, such as calculating an average position or applying advanced filtering techniques to determine the most accurate location.
11. An apparatus comprising: at least one memory configured to store multiple geolocation measurements based on cross-ambiguity function (CAF) detection, the geolocation measurements associated with at least one signal from at least one signal source that is received by multiple receivers; and at least one processing device configured to: associate related geolocation measurements to form at least one collection of related geolocation measurements, each collection of related geolocation measurements associated with a common one of the at least one signal received by at least some of the receivers; and perform geolocation using the at least one collection of related geolocation measurements to identify one or more geolocations of the at least one signal source; wherein, to associate the related geolocation measurements, the at least one processing device is configured to: form multiple observation groups, each observation group containing geolocation measurements that might be related to one another; identify geolocation measurements from the observation groups satisfying one or more criteria; and place the identified geolocation measurements into one or more refined observation groups, each refined observation group containing a collection of related geolocation measurements; and wherein the one or more criteria include at least one of: a criterion that each collection of related geolocation measurements includes one geolocation measurement from each of multiple distinct pairs of receivers; and a criterion that each collection of related geolocation measurements has time difference of arrival (TDOA) or frequency difference of arrival (FDOA) measurements that sum to a value within a threshold amount of zero.
This invention relates to geolocation systems that use cross-ambiguity function (CAF) detection to determine the position of signal sources. The problem addressed is the challenge of accurately identifying and grouping related geolocation measurements from multiple receivers to improve the precision of signal source localization. The apparatus includes a memory storing multiple geolocation measurements derived from CAF detection, where these measurements are associated with signals received by multiple receivers. A processing device processes these measurements by first forming observation groups, each containing geolocation measurements that may be related. The processing device then identifies measurements from these groups that meet specific criteria, such as having measurements from multiple distinct receiver pairs or TDOA/FDOA measurements that sum to a value close to zero. These identified measurements are placed into refined observation groups, each representing a collection of related geolocation measurements associated with a common signal. The processing device then uses these refined groups to perform geolocation, determining the position of the signal source with improved accuracy. This approach enhances geolocation accuracy by systematically filtering and grouping measurements to reduce errors and improve signal source localization.
12. An apparatus comprising: at least one memory configured to store multiple geolocation measurements based on cross-ambiguity function (CAF) detection, the geolocation measurements associated with at least one signal from at least one signal source that is received by multiple receivers; and at least one processing device configured to: associate related geolocation measurements to form at least one collection of related geolocation measurements, each collection of related geolocation measurements associated with a common one of the at least one signal received by at least some of the receivers; and perform geolocation using the at least one collection of related geolocation measurements to identify one or more geolocations of the at least one signal source; wherein, to associate the related geolocation measurements, the at least one processing device is configured to: form multiple observation groups, each observation group containing geolocation measurements that might be related to one another; identify geolocation measurements from the observation groups satisfying one or more criteria; place the identified geolocation measurements into one or more refined observation groups, each refined observation group containing a collection of related geolocation measurements; identify geolocation measurements from the observation groups that are associated with prior geolocation measurements previously used to determine at least one prior geolocation; and place the identified geolocation measurements into one or more additional refined observation groups.
This invention relates to geolocation systems that use cross-ambiguity function (CAF) detection to determine the position of signal sources. The problem addressed is the challenge of accurately identifying and grouping related geolocation measurements from multiple receivers to improve the precision of signal source localization. The apparatus includes memory storing multiple geolocation measurements derived from CAF detection of signals received by multiple receivers. A processing device processes these measurements by first forming multiple observation groups, where each group contains measurements that may be related. The device then identifies measurements within these groups that meet specific criteria, such as temporal or spatial proximity, and places them into refined observation groups. These refined groups represent collections of related measurements associated with a common signal. Additionally, the device identifies measurements linked to prior geolocation determinations and incorporates them into further refined groups. Finally, the apparatus performs geolocation using these refined groups to determine the precise location of the signal source. This approach enhances accuracy by systematically filtering and grouping measurements, reducing noise and improving the reliability of geolocation results. The method leverages historical data and refined grouping techniques to refine signal source tracking.
13. The apparatus of claim 12 , wherein, to associate the related geolocation measurements, the at least one processing device is further configured to: identify remaining geolocation measurements from the observation groups that do not satisfy the one or more criteria and that are not associated with the prior geolocation measurements; and determine whether any of the remaining geolocation measurements are related to one another.
This invention relates to geolocation measurement processing, specifically improving the accuracy and reliability of geolocation data by associating related measurements. The problem addressed is the presence of geolocation measurements that do not meet predefined criteria or are not initially associated with prior measurements, leading to incomplete or fragmented geolocation data. The solution involves an apparatus with at least one processing device configured to analyze geolocation measurements grouped into observation groups. The apparatus identifies measurements that do not satisfy the criteria and are unassociated with prior measurements, then determines whether these remaining measurements are related to one another. This process ensures that all relevant geolocation data is properly linked, enhancing the overall accuracy and coherence of the geolocation dataset. The apparatus may also include a memory storing the geolocation measurements and a communication interface for receiving or transmitting the data. The criteria for association may include factors such as proximity, time synchronization, or signal strength, ensuring that only relevant measurements are linked. By systematically associating related measurements, the invention improves the reliability of geolocation tracking and analysis.
14. The apparatus of claim 12 , wherein, to associate the related geolocation measurements, the at least one processing device is further configured to: place the geolocation measurements into different buckets, the geolocation measurements in each bucket associated with a common area of interest and based on signal receipt within a specified time window; place the geolocation measurements from the buckets into different groups, each group containing geolocation measurements from the same bucket, the geolocation measurements in each group associated with an approximately equal pulse width and an approximately equal frequency; and place the geolocation measurements from the groups into different subgroups, each subgroup containing geolocation measurements from the same group, the geolocation measurements in each subgroup associated with a common pair of receivers and associated with approximately equal time difference of arrival (TDOA) or frequency difference of arrival (FDOA) measurements.
This invention relates to a system for analyzing and associating geolocation measurements to improve accuracy in tracking or identifying signal sources. The problem addressed is the challenge of accurately correlating multiple geolocation measurements from different receivers, especially when dealing with signals that may have varying characteristics or originate from the same source. The system processes geolocation measurements by organizing them into hierarchical groupings to identify patterns and relationships. The system first categorizes geolocation measurements into different buckets, where each bucket contains measurements linked to a common area of interest and collected within a specified time window. This initial grouping helps filter measurements that are spatially and temporally related. Next, the measurements within each bucket are further divided into groups based on signal characteristics, such as pulse width and frequency, ensuring that measurements in the same group share similar signal properties. Finally, the measurements within each group are refined into subgroups, where each subgroup contains measurements from the same pair of receivers and exhibits approximately equal time difference of arrival (TDOA) or frequency difference of arrival (FDOA) values. This hierarchical clustering allows for precise association of measurements, improving the accuracy of geolocation tracking by distinguishing between different signal sources or events. The system enhances signal analysis by systematically organizing measurements to identify meaningful patterns and reduce ambiguity in geolocation data.
15. A non-transitory computer readable medium containing instructions that when executed cause at least one processor to: obtain multiple geolocation measurements based on cross-ambiguity function (CAF) detection, the geolocation measurements associated with at least one signal from at least one signal source that is received by multiple receivers; associate related geolocation measurements to form at least one collection of related geolocation measurements, each collection of related geolocation measurements associated with a common one of the at least one signal received by at least some of the receivers; and perform geolocation using the at least one collection of related geolocation measurements to identify one or more geolocations of the at least one signal source; wherein the instructions that when executed cause the at least one processor to associate the related geolocation measurements comprise instructions that when executed cause the at least one processor to: place the geolocation measurements into different buckets, the geolocation measurements in each bucket associated with a common area of interest and based on signal receipt within a specified time window; place the geolocation measurements from the buckets into different groups, each group containing geolocation measurements from the same bucket, the geolocation measurements in each group associated with an approximately equal pulse width and an approximately equal frequency; and place the geolocation measurements from the groups into different subgroups, each subgroup containing geolocation measurements from the same group, the geolocation measurements in each subgroup associated with a common pair of receivers and associated with approximately equal time difference of arrival (TDOA) or frequency difference of arrival (FDOA) measurements.
This invention relates to geolocation systems that use cross-ambiguity function (CAF) detection to determine the position of signal sources. The problem addressed is the challenge of accurately identifying the location of signal sources, such as transmitters, when multiple receivers capture signals with varying characteristics. The solution involves processing geolocation measurements from multiple receivers to improve accuracy and reliability. The system obtains geolocation measurements based on CAF detection, which analyzes signals received by multiple receivers to estimate the source's location. These measurements are then organized into collections of related data. First, measurements are placed into different buckets, where each bucket corresponds to a specific area of interest and a defined time window. Next, measurements within each bucket are grouped based on similar pulse width and frequency characteristics. Finally, measurements within each group are further divided into subgroups based on common receiver pairs and similar time difference of arrival (TDOA) or frequency difference of arrival (FDOA) values. By structuring the data in this hierarchical manner, the system can more effectively filter and analyze the measurements to determine the precise geolocation of the signal source. This approach enhances accuracy by reducing noise and improving the consistency of the measurements used for geolocation.
16. The non-transitory computer readable medium of claim 15 , further containing instructions that when executed cause the at least one processor to at least one of: generate a graphical display based on at least one of the one or more geolocations; and generate a report containing at least one of the one or more geolocations.
A system and method for processing and utilizing geolocation data involves capturing and analyzing location information from one or more devices. The system collects geolocation data, which may include coordinates, timestamps, and other contextual information, and processes this data to extract meaningful insights. The processed data can be used to generate visual representations, such as maps or charts, to display the geolocations in a user-friendly format. Additionally, the system can compile the geolocation data into structured reports, which may include summaries, trends, or other analytical outputs. The reports and graphical displays can be customized based on user preferences or specific use cases, such as tracking movement patterns, monitoring asset locations, or analyzing spatial distributions. The system may also integrate with external databases or APIs to enhance the geolocation data with additional contextual information, such as points of interest or environmental factors. The overall goal is to provide a comprehensive tool for visualizing and reporting geolocation data to support decision-making in various applications, including logistics, security, and environmental monitoring.
17. The non-transitory computer readable medium of claim 15 , wherein the instructions that when executed cause the at least one processor to associate the related geolocation measurements further comprise: instructions that when executed cause the at least one processor to form multiple observation groups, each observation group containing geolocation measurements that might be related to one another.
The invention relates to a system for processing geolocation measurements using a non-transitory computer-readable medium containing executable instructions for a processor. The core problem addressed is the efficient organization and association of geolocation data points that may be related to each other, such as measurements from the same device, time period, or geographic area. The solution involves forming multiple observation groups, where each group contains geolocation measurements that are potentially related. This grouping allows for more accurate analysis, filtering, or processing of the data by treating related measurements as a single unit. The instructions enable the processor to dynamically create these groups based on criteria such as temporal proximity, spatial proximity, or shared attributes, improving the reliability of geolocation-based applications like tracking, mapping, or location-based services. The approach ensures that only relevant measurements are considered together, reducing noise and enhancing the precision of downstream processing tasks.
18. The non-transitory computer readable medium of claim 17 , wherein the instructions that when executed cause the at least one processor to associate the related geolocation measurements further comprise instructions that when executed cause the at least one processor to: identify geolocation measurements from the observation groups satisfying one or more criteria; and place the identified geolocation measurements into one or more refined observation groups, each refined observation group containing a collection of related geolocation measurements.
This invention relates to geolocation data processing, specifically improving the accuracy and reliability of geolocation measurements by refining observation groups. The problem addressed is the presence of noise, outliers, or inconsistencies in geolocation data collected from multiple sources, which can lead to inaccurate positioning or tracking. The solution involves a method for refining geolocation measurements by grouping related data points and filtering them based on predefined criteria. The system processes raw geolocation measurements, which may include timestamps, coordinates, and other metadata, and organizes them into initial observation groups. These groups are then analyzed to identify measurements that meet specific criteria, such as proximity, time correlation, or signal strength. The identified measurements are then placed into refined observation groups, where each group contains only the most relevant and reliable geolocation data. This refinement process enhances the accuracy of subsequent geolocation calculations, such as determining a device's position or tracking movement patterns. The invention may be implemented as a software module executed by a processor, where the instructions for refining geolocation measurements are stored on a non-transitory computer-readable medium. The refined observation groups can be used for various applications, including navigation, asset tracking, or location-based services, where precise geolocation data is critical. The method ensures that only high-quality measurements are used, reducing errors and improving the overall reliability of geolocation systems.
19. The non-transitory computer readable medium of claim 18 , wherein the instructions that when executed cause the at least one processor to associate the related geolocation measurements further comprise instructions that when executed cause the at least one processor to: identify geolocation measurements from the observation groups that are associated with prior geolocation measurements previously used to determine at least one prior geolocation; and place the identified geolocation measurements into one or more additional refined observation groups.
This invention relates to geolocation tracking systems that improve accuracy by refining observation groups using prior geolocation data. The problem addressed is the inherent inaccuracy in geolocation measurements due to environmental factors, device limitations, or signal interference, which can lead to unreliable positioning data. The solution involves a method for processing geolocation measurements by associating them with prior geolocation data to enhance precision. The system collects geolocation measurements from various sources, such as GPS, Wi-Fi, or cellular signals, and organizes them into observation groups. These groups are then refined by identifying measurements that correlate with previously used geolocation data from prior tracking sessions. The identified measurements are placed into additional refined observation groups, which are then used to determine a more accurate geolocation. This refinement process leverages historical data to filter out outliers and improve the reliability of the final geolocation output. The system may also apply statistical or machine learning techniques to further enhance the accuracy of the refined observation groups. The overall approach ensures that geolocation tracking is more consistent and reliable over time by continuously improving the quality of the input data.
20. The non-transitory computer readable medium of claim 19 , wherein the instructions that when executed cause the at least one processor to associate the related geolocation measurements further comprise instructions that when executed cause the at least one processor to: identify remaining geolocation measurements from the observation groups that do not satisfy the one or more criteria and that are not associated with the prior geolocation measurements; and determine whether any of the remaining geolocation measurements are related to one another.
This invention relates to geolocation data processing, specifically improving the accuracy and reliability of geolocation measurements by associating related measurements and identifying outliers. The problem addressed is the presence of noisy or inconsistent geolocation data, which can lead to inaccurate positioning or tracking. The solution involves a method for analyzing groups of geolocation measurements to determine their relationships and filter out unreliable data. The system processes observation groups containing geolocation measurements and applies one or more criteria to identify prior geolocation measurements that meet these criteria. These prior measurements are then associated with other measurements in the group. For measurements that do not satisfy the criteria and are not already associated, the system further analyzes them to determine if any are related to one another. This step ensures that even measurements initially deemed unreliable may still be valid if they share a relationship with other measurements. The process helps improve the overall accuracy of geolocation data by reducing noise and identifying meaningful patterns. The invention is implemented via a non-transitory computer-readable medium containing instructions for a processor to perform these operations.
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September 10, 2020
April 5, 2022
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